The Interacting Multiple Model Extended Kalman Filter (IMMEKF) with measurement fusion algorithm is presented to track the target using the infrared Search & Track (IRST) and radar data where the state and measurements have nonlinear relationship. The architecture of this IMMEKF algorithm is similar to IMMKF except the mode condition filter and the preprocessing of the measurements. Since the state and measurements are nonlinear relationship, EKF is used as mode condition filter instead of linear Kalman filter. Since the measurements are obtained from two sensors, these measurements are fused using measurement fusion algorithm and fed to the IMMEKF. To bring out the benefits of measurement fusion, the performance of IMMEKF with following cases: 1) Measurement fusion (MF) where the angular measurements are fused with corresponding measurement noise covariance & range is taken from radar, 2) Selective measurements (SM) where the angular measurements are taken from IRST & range is taken from radar and 3) radar where both angular and range are taken from radar are considered and evaluated. The tracker performance evaluation metrics are within the acceptable ranges that shows the IMMEKF robustness. It is concluded that IMMEKF with measurement fusion would be useful to track the maneuvering target when target is being tracked by the IRST and radar. Robustness of the IMMEKF with MF is evaluated in presence of clutter and also with data loss in IRST or/and radar. It is concluded that there are larger errors in estimated target states when IMMEKF with radar measurements alone are used in state estimation. This shows the necessity of fusion of IRST and radar in the process of target tracking.